Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)

• URL: http://arxiv.org/abs/2106.06174v3
• Date: Wed, 13 Dec 2023 09:16:47 GMT
• Title: Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
• Authors: Danial Yazdani (1), Michalis Mavrovouniotis (2), Changhe Li (3), Wenjian Luo (4), Mohammad Nabi Omidvar (5), Amir H. Gandomi (6), Trung Thanh Nguyen (7), Juergen Branke (8), Xiaodong Li (9), Shengxiang Yang (10), and Xin Yao (11) ((1) Faculty of Engineering & Information Technology, University of Technology Sydney,(2) ERATOSTHENES Centre of Excellence, (3) School of Automation, China University of Geosciences, (4) Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology and Peng Cheng Laboratory, (5) School of Computing, University of Leeds, and Leeds University Business School, (6) Faculty of Engineering & Information Technology, University of Technology Sydney and University Research and Innovation Center (EKIK), Obuda University, (7) Liverpool Logistics, Offshore and Marine (LOOM) Research Institute, Faculty of Engineering and Technology, School of Engineering, Liverpool John Moores University, (8) Warwick Business school, University of Warwick, (9) School of Science (Computer Science), RMIT University, (10) Center for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, (11) Research Institute of Trustworthy Autonomous Systems (RITAS), and Guangdong Provincial Key Laboratory of Brain inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, and CERCIA, School of Computer Science, University of Birmingham)
• Abstract summary: This document introduces the Generalized Moving Benchmark (GMPB) GMPB is adept at generating landscapes with a broad spectrum of characteristics. This document delves into the intricacies of GMPB, detailing its myriad ways in which its parameters can be tuned to produce these diverse landscape characteristics.
• Score: 5.1812733319583915
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This document introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating continuous dynamic optimization problem instances that is used for the CEC 2024 Competition on Dynamic Optimization. GMPB is adept at generating landscapes with a broad spectrum of characteristics, offering everything from unimodal to highly multimodal landscapes and ranging from symmetric to highly asymmetric configurations. The landscapes also vary in texture, from smooth to highly irregular surfaces, encompassing diverse degrees of variable interaction and conditioning. This document delves into the intricacies of GMPB, detailing the myriad ways in which its parameters can be tuned to produce these diverse landscape characteristics. GMPB's MATLAB implementation is available on the EDOLAB Platform.

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